Artificial Intelligence vs. Machine Learning: What’s the Difference?

Artificial intelligence (AI) is a broad term used to describe various types of virtual “intelligence” designed to replicate aspects of human cognitive abilities.

Machine learning (ML) is a type of AI, but it’s not the kind that we often see in sci-fi movies. Instead, it’s a technique used to develop AI, allowing the system to learn from data and improve on its own over time.

What Is Artificial Intelligence?

Artificial intelligence is the measure of a computer’s intellectual ability. But there isn’t a scientific body that decides what is or is not, technically, AI; the term is defined by whoever is using it.

The Encyclopedia Britannica defines artificial intelligence as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” In this sense, a computer that can make predictions is artificially intelligent.

Britannica, however, goes on to note that the “term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience.”

In everyday conversations and popular culture, AI is usually depicted as advanced humanoid robots—androids that talk, think, and feel like humans. While these futuristic depictions represent AI, they are highly sophisticated forms of it. To create such advanced AI, foundational technologies like machine learning are essential.

What Is Machine Learning?

While AI is a measure of a computer’s intellectual ability, machine learning is a type of artificial intelligence used to build intellectual ability in computers.

Investopedia defines ML as “the concept that a computer program can learn and adapt to new data without human intervention.” An example you’ve likely used is when you search for specific photos in your phone’s photo library. You can search for ‘tree,’ and pictures of trees will show up without you having said to the phone, “This is a tree.”

Machine learning is powered by hubs of interconnected computers or supercomputers processing massive quantities of data to train a program to give a particular output with a given input.

Examples of Artificial Intelligence vs. Machine Learning

In 2011, a new challenger. IBM’s Watson supercomputer defeated two champions of the long-running Jeopardy game show.

This room-sized machine could understand and answer the complicated, specific questions characteristic of the show better than the best players on the show at the time. Watson is an example of artificial intelligence.

IBM offers a service called IBM Watson Studio that allows third parties to use their technology to build, train, and test predictive software. Watson needs to independently “understand” and “respond” to human writing and speech, which is an example of machine learning.

Watson, the supercomputer, is artificial intelligence, while its ability to understand language and respond using it is machine learning, much like a virtual assistant like Alexa uses to talk to you.

Artificial intelligence, as portrayed in the movies, is much more advanced than IBM’s Watson. However, machine learning will be an essential component of higher-level AI like robots and androids, just as it’s an integral component of Watson.